Direct causal structure extraction from pairwise interaction patterns in NAT modeling Bayesian networks

DOI10.1016/j.ijar.2018.11.016zbMath1452.68163OpenAlexW2902969750WikidataQ128865489 ScholiaQ128865489MaRDI QIDQ1726348

Yang Xiang

Publication date: 20 February 2019

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2018.11.016

zbMATH Keywords

Bayesian networksprobabilistic inferencemachine learninggraphical modelscausal modelsnon-impeding noisy-AND trees

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (2)

Learning tractable NAT-modeled Bayesian networks ⋮ The extended recursive noisy or model: static and dynamic considerations

Cites Work

• An intercausal cancellation model for Bayesian-network engineering
• Importance sampling in Bayesian networks using probability trees.
• Non-impeding noisy-AND tree causal models over multi-valued variables
• Efficient probabilistic inference in Bayesian networks with multi-valued NIN-AND tree local models
• Compression of General Bayesian Net CPTs
• An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper
• A differential approach to inference in Bayesian networks

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